A big data MapReduce framework for fault diagnosis in cloud-based manufacturing

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 4th March 2016, available online: http://www.tandfonline.com/10.1080/00207543.2016.1153166.This research develops a MapReduce framework for automatic pattern recognition based on fault diagnosis by solving data imbalance problem in a cloud-based manufacturing (CBM). Fault diagnosis in a CBM system significantly contributes to reduce the product testing cost and enhances manufacturing quality. One of the major challenges facing the big data analytics in cloud-based manufacturing is handling of datasets, which are highly imbalanced in nature due to poor classification result when machine learning techniques are applied on such datasets. The framework proposed in this research uses a hybrid approach to deal with big dataset for smarter decisions. Furthermore, we compare the performance of radial basis function based Support Vector Machine classifier with standard techniques. Our findings suggest that the most important task in cloud-based manufacturing, is to predict the effect of data errors on quality due to highly imbalance unstructured dataset. The proposed framework is an original contribution to the body of literature, where our proposed MapReduce framework has been used for fault detection by managing data imbalance problem appropriately and relating it to firm’s profit function. The experimental results are validated using a case study of steel plate manufacturing fault diagnosis, with crucial performance matrices such as accuracy, specificity and sensitivity. A comparative study shows that the methods used in the proposed framework outperform the traditional ones

Enterprise competence organization schema: publishing the published competences

This article was published in the journal Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture [Sage © IMechE]. The definitive version is available at: http://dx.doi.org/10.1177/09544054JEM2097Competence is a standardized way to define the profile of an enterprise. Understanding and auditing competences acquired, required, and desired by a company and further representing them in a structured manner is a beneficial step for enhancing the company's performance. Ontology is emerging as an effective tool to structure competences for comprehensive and transportable machine understanding. In the present paper, ECOS (Enterprise Competence Organization Schema) is presented as a mechanism to capture enterprise competence in a manner understandable by computers. The objective behind this concept is to create a web of machine-readable pages describing basic information and competences of enterprises with sets of interconnected data and semantic models. The ECOS ontology captures enterprise competences using a consistent and comprehensive list of concepts and vocabulary and converts them into a semantic web resource using the Web Ontology Language (OWL). The novel concept of an ECOS-card and ECOS-form is proposed and used for developing and publishing enterprise competences. Examples from real-life enterprise applications of ECOS are also shown in the paper